Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Survival Tree01:19

Survival Tree

73
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
73
Weighted Mean00:57

Weighted Mean

5.0K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
5.0K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

45
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
45
Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

2.5K
In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
2.5K
Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

2.8K
The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
The relation  between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...
2.8K
Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Effect of Inorganic Fillers on Electrical and Mechanical Properties of Ceramizable Silicone Rubber.

Polymers·2024
Same author

Positron emission tomography with Pittsburgh compound B in diagnosis of early stage Alzheimer's disease.

Cell biochemistry and biophysics·2010
Same author

[Preparation of enteric nanoparticles of Schisandra total lignanoids and preliminary study on its pharmacokinetics].

Yao xue xue bao = Acta pharmaceutica Sinica·2010
Same author

[A survey of health effects on population exposure to a dust event in Beijing City].

Wei sheng yan jiu = Journal of hygiene research·2010
Same author

Effects of beta-ionone on mammary carcinogenesis and antioxidant status in rats treated with DMBA.

Nutrition and cancer·2010
Same author

[The effect of urokinase on hepatic fibrogenesis in rats].

Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology·2009

相关实验视频

Updated: Jun 14, 2025

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities
08:08

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Published on: May 10, 2017

14.7K

WI-TMLEGA:体重初始化和培训方法基于和学习率调整的和学习率调整.

Hongchuan Tang1, Zhongguo Li1, Qi Wang1,2

  • 1School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang 212100, China.

Entropy (Basel, Switzerland)
|August 29, 2024
PubMed
概括

本研究介绍了一种用于重量初始化和多层感知子 (MLP) 模型中的动态学习速率的增法. 这种方法显著提高了大型模型的培训效率和识别精度.

关键词:
在MNIST数据集中,MNIST数据集包含了各种数据.学习率的学习率是什么多层感知器多层感知器重量初始化初始化重量更新重量更新

更多相关视频

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.0K
Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

33.7K

相关实验视频

Last Updated: Jun 14, 2025

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities
08:08

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Published on: May 10, 2017

14.7K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.0K
Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

33.7K

科学领域:

  • 机器学习 机器学习
  • 人工智能的人工智能
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 长时间的培训时间和低的认可率是大型模型应用的重大挑战.
  • 当前的重量初始化和学习速度调整方法可能对复杂模型来说不是最优的.
  • 多层感知器 (MLP) 模型是评估新培训技术的基本例子.

研究的目的:

  • 为大型模型提出和评估一种新的权力训练方法.
  • 解决延长培训时间和低认可率的问题.
  • 为了证明增益对体重初始化和动态学习速度调整的有效性.

主要方法:

  • 利用增益来取代重量初始化的随机初始值.
  • 实施了一个增量学习率策略,用于动态加权更新.
  • 训练并验证了使用MNIST手写数字数据集的多层感知子 (MLP) 模型.

主要成果:

  • 与随机初始化相比,拟议的初始化方法提高了39.8%的培训效率.
  • 实现了最大识别精度增加8.9%.
  • 在MNIST数据集上证明了显著的性能增长.

结论:

  • 拟议的基于益的权力训练方法对于大型模型应用是可行的和有效的.
  • 该方法提供了一个可行的解决方案,以提高培训效率和认可准确性.
  • 进一步的研究可以在其他复杂的深度学习架构中探索这种技术.